DOCSLIB.ORG

The Accuracy and Efficiency of the Consensus Forecasts: a Further Application and Extension of the Pooled Approach

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Accuracy and Efficiency of the Consensus Forecasts: a Further Application and Extension of the Pooled Approach Dis cus si on Paper No. 07-058 The Accuracy and Efficiency of the Consensus Forecasts: A Further Application and Extension of the Pooled Approach Philipp Ager, Marcus Kappler, and Steffen Osterloh Dis cus si on Paper No. 07-058 The Accuracy and Efficiency of the Consensus Forecasts: A Further Application and Extension of the Pooled Approach Philipp Ager, Marcus Kappler, and Steffen Osterloh Download this ZEW Discussion Paper from our ftp server: ftp://ftp.zew.de/pub/zew-docs/dp/dp07058.pdf Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar. Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. Non-technical summary The last decade has seen marked economic fluctuations in the major industrial countries, which regularly present business cycle forecasters with a challenge. In this paper we are interested in how professional forecasters managed to predict GDP and price developments during the last decade. To this end, we explore the accuracy and evolution of the Consensus Forecast for twelve industrial countries for the years 1996 to 2006. This pooled forecast has the main advantage that it offers monthly publications of revised forecasts for the current and the next year, so that an explicit revision process of 24 forecasts for every target year can be observed. The theoretical and econometric analysis is based on the framework of Davies and Lahiri (1995) and Clements et al. (2006). The latter employ a pooling procedure which permits the evaluation of all forecasts for each target variable over 24 horizons simultaneously. Adopting this methodology allows us to draw conclusions on evaluating systematic forecast bias and, by applying a test on the predictability of forecast revisions, on the efficient use of new information for all forecast horizons jointly. It is shown how the pooled approach needs to be adjusted in order to accommodate the forecasting scheme of the Consensus Forecasts. Furthermore, the pooled approach is extended by a sequential test with the purpose of detecting the critical horizon beyond which the forecast should be regarded as biased. Moreover, we show how the pooled tests for the predictability of forecast revisions can be improved by taking heteroscedasticity in the form of target year-specific variances of macroeconomic shocks into account. In the empirical part we first present results in the form of analytical confidence intervals surrounding the horizon-specific bias estimates which allow intuitive and meaningful interpretations. The test for common bias reveals that several countries show biased forecasts, especially with forecasts covering more than 12 months. These results partially confirm the presumption that the macroeconomic forecasts for the past 10 years were severely affected by the pronounced shocks in that period. The fact that for individual countries systematic biases can be observed by applying the Consensus Forecasts reveals that in these countries the forecasting industry on the whole was not able to cope with the shocks specific to the past ten years. Revisions of past months for GDP growth forecast have significant explanatory power for current revisions for almost all countries. For inflation revisions we find significant past revisions for some countries. Overall, our results imply that a lack of information efficiency is more severe for GDP forecasts than for inflation forecasts. The accuracy and efficiency of the Consensus Forecasts: A further application and extension of the pooled approach October 2007 Philipp Ager GSE, Universitat Pompeu Fabra, Barcelona Marcus Kappler1 Centre for European Economic Research (ZEW), Mannheim Steffen Osterloh Centre for European Economic Research (ZEW), Mannheim Abstract: In this paper we analyze the macroeconomic forecasts of the Consensus Forecasts for 12 countries over the period from 1996 to 2006 regarding bias and information efficiency. A pooled approach is employed which permits the evaluation of all forecasts for each target variable over 24 horizons simultaneously. It is shown how the pooled approach needs to be adjusted in order to accommodate the forecasting scheme of the Consensus Forecasts. Furthermore, the pooled approach is extended by a sequential test with the purpose of detecting the critical horizon after which the forecast should be regarded as biased. Moreover, heteroscedasticity in the form of year-specific variances of macroeconomic shocks is taken into account. The results show that in the analyzed period which was characterized by pronounced macroeconomic shocks, several countries show biased forecasts, especially with forecasts covering more than 12 months. In addition, information efficiency has to be rejected in almost all cases. Keywords: business cycle forecasting, forecast evaluation, Consensus Forecasts JEL-Classification: C 52, E 32, E 37 1 Corresponding author. P.O. Box 103443, D-68034, Mannheim, Germany, E-Mail: [email protected]. For helpful feedback, we thank François Laisney and the audience at the International Workshop in Economics and Finance (IWEF 2007) at Tripolis, Greece, 16 June 2007. All remaining errors are the responsibility of the authors. 1 Introduction The last decade has seen marked economic fluctuations in the major industrial countries, starting from the new economy boom beginning in the mid-nineties, followed by a lengthy downturn due to the burst of the dot-com bubble and accelerated by a number of events like 9/11 and concluding with the recent economic recovery. Such economic fluctuations regularly present business cycle forecasters with a challenge. In this paper we are interested in how professional forecasters managed to predict GDP and price developments during the last decade. To this end, we explore the biasedness and efficiency of the Consensus Forecast for twelve industrial countries for the years 1996 to 2006. The Consensus Forecast is a pooled forecast based on a monthly survey among professional forecasters from the respective country. Due to its aggregation it is often regarded as the market’s opinion on the future development of the economy. One main advantage the Consensus Forecasts offer to our analysis is that, because of the monthly publication of revised forecasts for the current and the next year, an explicit revision process of 24 forecasts for every target year can be observed. Moreover, due to international standardization regarding methodology and date of publication, these forecasts are highly relevant for international comparisons. This kind of forecasts has received increasing attention in the forecast evaluation literature in the past years, starting with Loungani (2001) who compares the Consensus Forecasts for industrialized and developing countries in the years 1989-1998 regarding accuracy, biasedness and efficiency. In a recent work, Batchelor (2007) addresses similar questions by analyzing the bias of the Consensus Forecasts in the years 1990-2005. He finds evidence for overoptimistic GDP forecasts in Japan, Germany, France and Italy, and no evidence for a bias of the inflation forecasts. The biasedness of individual forecasters is a very popular object of investigation (see, for instance, Batchelor, 2001, for OECD and IMF forecasts; a survey on national studies can be found in Fildes & Stekler, 2002). The frequently found characteristic of systematically biased forecasts is usually attributed to error sources on the individual level such as model misspecifications, herding or political biases (see Stekler, 2007, for an overview). However, the studies applying the Consensus Forecasts use a pooled forecast, where these individual biases are typically expected to cancel out. Therefore, the biases found by Batchelor (2007), which are unequal in size between the countries, do not hint to irrationality on the individual level, but can be attributed to a common wrong dealing with 1 country-specific external influences such as declining growth trends, which were shared by all forecasters in a country. As the Consensus Forecasts constitute a fixed-event forecast, with every target year forecasted separately from 24 horizons before, it complicates the empirical analysis. Batchelor (2007) applies a conventional approach, by testing single forecast horizons individually for biasedness. This may be accomplished by the Mincer-Zarnowitz test (Mincer & Zarnowitz, 1969), or by a more general t-test as introduced by Clements (2005, p.6). However, this kind of test has one decisive weak point for the high-frequency fixed-event forecasts of the Consensus Forecasts: Either all 24 horizons are tested individually, and the comparative results lose in explanatory power due to the complexity of 24 tests with potentially different results. Or, as done in Batchelor (2007), only selected horizons out of the 24 available are tested and compared, but then a loss of information occurs. To cope with this problem, in this paper a cutting edge pooling method proposed by Clements et al. (2007) and Davis & Lahiri (1995) is applied. The second question of this paper, the efficiency of the forecasts, has received more attention in the literature regarding the application to fixed-event forecasts in the form of the Nordhaus test (Nordhaus, 1987) on the unpredictability of forecast revisions. Further applications of this methodology on the pooling of fixed-event forecasts over different target years can be found in Clements (1997) and Harvey et al. (2001). Isiklar et al. (2006) are the first who analyze Consensus Forecasts for 18 industrialized countries from 1989 to 2004 and who consider a pooling over several countries. Again, a pooled approach based on the works of Clements et al.
Load more

Recommended publications
  • WWP Wolfsburg Working Papers No. 12-01
    WWP Wolfsburg Working Papers No. 12-01 The improvement of annual economic forecasts by using non-annual indicators An empirical investigation for the G7 states Johannes Scheier, 2012 The improvement of annual economic forecasts by using non- annual indicators An Empirical Investigation for the G7 states Johannes Scheier Faculty of Business, Chair in Finance, Ostfalia University of Applied Sciences, D-38440 Wolfsburg, Germany Email: [email protected] Phone: +49 5361 8922 25450 Abstract A key demand made of business cycle forecasts is the efficient processing of freely available information. On the basis of two early indicators, this article shows that these opportunities are not being exploited. To this end, consensus forecasts for the economies of the G7 states from 1991-2009 were examined in this study. According to the present state of research, the forecasts are of moderate quality depending on the forecast horizon. The situation is different with regard to the early indicators which were examined. Both the results of a worldwide survey among experts by the Ifo Institute as well as the Composite Leading Indicators of the OECD exhibit a measurable correlation to the economic development considerably earlier for all countries. If viewed simultaneously, it can be seen that the forecast quality for most G7 states could be improved by taking the early indicators into consideration. Keywords: adjusting forecasts, business cycles, macroeconomic forecasts, evaluating forecasts, forecast accuracy, forecast efficiency, forecast monitoring, panel data, time series JEL classification: E17, E32, E37, E60 I. Introduction A wide range of demands are made of business cycle forecasts. The main demand is for them to be of good quality so that they can serve as a planning basis for states, companies and financial market participants.
    [Show full text]
  • Managing Functional Biases in Organizational Forecasts: a Case Study of Consensus Forecasting in Supply Chain Planning
    07-024 Managing Functional Biases in Organizational Forecasts: A Case Study of Consensus Forecasting in Supply Chain Planning Rogelio Oliva Noel Watson Copyright © 2007 by Rogelio Oliva and Noel Watson. Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author. Managing Functional Biases in Organizational Forecasts: A Case Study of Consensus Forecasting in Supply Chain Planning Rogelio Oliva Mays Business School Texas A&M University College Station, TX 77843-4217 Ph 979-862-3744 | Fx 979-845-5653 [email protected] Noel Watson Harvard Business School Soldiers Field Rd. Boston, MA 02163 Ph 617-495-6614 | Fx 617-496-4059 [email protected] Draft: December 14, 2007. Do not quote or cite without permission from the authors. Managing Functional Biases in Organizational Forecasts: A Case Study of Consensus Forecasting in Supply Chain Planning Abstract To date, little research has been done on managing the organizational and political dimensions of generating and improving forecasts in corporate settings. We examine the implementation of a supply chain planning process at a consumer electronics company, concentrating on the forecasting approach around which the process revolves. Our analysis focuses on the forecasting process and how it mediates and accommodates the functional biases that can impair the forecast accuracy. We categorize the sources of functional bias into intentional, driven by misalignment of incentives and the disposition of power within the organization, and unintentional, resulting from informational and procedural blind spots.
    [Show full text]
  • Anchoring Bias in Consensus Forecasts and Its Effect on Market Prices
    Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. Anchoring Bias in Consensus Forecasts and its Effect on Market Prices Sean D. Campbell and Steven A. Sharpe 2007-12 NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers. ANCHORING BIAS IN CONSENSUS FORECASTS AND ITS EFFECT ON MARKET PRICES* Sean D. Campbell Steven A. Sharpe February 2007 Previous empirical studies that test for the “rationality” of economic and financial forecasts generally test for generic properties such as bias or autocorrelated errors, and provide limited insight into the behavior behind inefficient forecasts. In this paper we test for a specific behavioral bias -- the anchoring bias described by Tversky and Kahneman (1974). In particular, we examine whether expert consensus forecasts of monthly economic releases from Money Market Services surveys from 1990-2006 have a tendency to be systematically biased toward the value of previous months’ data releases. We find broad- based and significant evidence for the anchoring hypothesis; consensus forecasts are biased towards the values of previous months’ data releases, which in some cases results in sizable predictable forecast errors. Then, to investigate whether the market participants anticipate the bias, we examine the response of interest rates to economic news.
    [Show full text]
  • Forecasting Spikes in Electricity Prices
    NCER Working Paper Series Forecasting Spikes in Electricity Prices T M Christensen A S Hurn K A Lindsay Working Paper #70 January 2011 Forecasting Spikes in Electricity Prices T M Christensen Department of Economics, Yale University A S Hurn School of Economics and Finance, Queensland University of Technology K A Lindsay Department of Mathematics, University of Glasgow. Abstract In many electricity markets, retailers purchase electricity at an unregulated spot price and sell to consumers at a heavily regulated price. Consequently the occurrence of extreme movements in the spot price represents a major source of risk to retailers and the accu- rate forecasting of these extreme events or price spikes is an important aspect of effective risk management. Traditional approaches to modeling electricity prices are aimed primarily at predicting the trajectory of spot prices. By contrast, this paper focuses exclusively on the prediction of spikes in electricity prices. The time series of price spikes is treated as a realization of a discrete-time point process and a nonlinear variant of the autoregressive conditional hazard (ACH) model is used to model this process. The model is estimated using half-hourly data from the Australian electricity market for the sample period 1 March 2001 to 30 June 2007. The estimated model is then used to provide one-step-ahead forecasts of the probability of an extreme event for every half hour for the forecast period, 1 July 2007 to 30 September 2007, chosen to correspond to the duration of a typical forward contract. The forecasting performance of the model is then evaluated against a benchmark that is consistent with the assumptions of commonly-used electricity pricing models.
    [Show full text]
  • Applying a Microfounded-Forecasting Approach to Predict Brazilian Inflation
    Applying a Microfounded-Forecasting Approach to Predict Brazilian Inflation Wagner Piazza Gaglianone, João Victor Issler and Silvia Maria Matos May, 2016 436 ISSN 1518-3548 CGC 00.038.166/0001-05 Working Paper Series Brasília n. 436 May 2016 p. 1-30 Working Paper Series Edited by Research Department (Depep) – E-mail: [email protected] Editor: Francisco Marcos Rodrigues Figueiredo – E-mail: [email protected] Co-editor: João Barata Ribeiro Blanco Barroso – E-mail: [email protected] Editorial Assistant: Jane Sofia Moita – E-mail: [email protected] Head of Research Department: Eduardo José Araújo Lima – E-mail: [email protected] The Banco Central do Brasil Working Papers are all evaluated in double blind referee process. Reproduction is permitted only if source is stated as follows: Working Paper n. 436. Authorized by Altamir Lopes, Deputy Governor for Economic Policy. General Control of Publications Banco Central do Brasil Comun/Dipiv/Coivi SBS – Quadra 3 – Bloco B – Edifício-Sede – 14º andar Caixa Postal 8.670 70074-900 Brasília – DF – Brazil Phones: +55 (61) 3414-3710 and 3414-3565 Fax: +55 (61) 3414-1898 E-mail: [email protected] The views expressed in this work are those of the authors and do not necessarily reflect those of the Banco Central or its members. Although these Working Papers often represent preliminary work, citation of source is required when used or reproduced. As opiniões expressas neste trabalho são exclusivamente do(s) autor(es) e não refletem, necessariamente, a visão do Banco Central do Brasil. Ainda que este artigo represente trabalho preliminar, é requerida a citação da fonte, mesmo quando reproduzido parcialmente.
    [Show full text]
  • Forecasting Macroeconomic Risks
    Federal Reserve Bank of New York Staff Reports Forecasting Macroeconomic Risks Patrick A. Adams Tobias Adrian Nina Boyarchenko Domenico Giannone Staff Report No. 914 February 2020 This paper presents preliminary findings and is being distributed to economists and other interested readers solely to stimulate discussion and elicit comments. The views expressed in this paper are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors. Forecasting Macroeconomic Risks Patrick A. Adams, Tobias Adrian, Nina Boyarchenko, and Domenico Giannone Federal Reserve Bank of New York Staff Reports, no. 914 February 2020 JEL classification: C22, E17, E37 Abstract We construct risks around consensus forecasts of real GDP growth, unemployment, and inflation. We find that risks are time-varying, asymmetric, and partly predictable. Tight financial conditions forecast downside growth risk, upside unemployment risk, and increased uncertainty around the inflation forecast. Growth vulnerability arises as the conditional mean and conditional variance of GDP growth are negatively correlated: downside risks are driven by lower mean and higher variance when financial conditions tighten. Similarly, employment vulnerability arises as the conditional mean and conditional variance of unemployment are positively correlated, with tighter financial conditions corresponding to higher forecasted unemployment and higher variance around the consensus forecast. Key words: macroeconomic uncertainty, quantile regressions, financial conditions _________________ Boyarchenko: Federal Reserve Bank of New York and CEPR (email: [email protected]). Adams: MIT Sloan (email: [email protected]). Adrian: International Monetary Fund and CEPR (email: [email protected]).
    [Show full text]
  • A New Approach to Predicting Analyst Forecast Errors: Do Investors Overweight Analyst Forecasts?$
    Journal of Financial Economics ] (]]]]) ]]]–]]] Contents lists available at SciVerse ScienceDirect Journal of Financial Economics journal homepage: www.elsevier.com/locate/jfec A new approach to predicting analyst forecast errors: Do investors overweight analyst forecasts?$ Eric C. So Massachusetts Institute of Technology, Sloan School of Management, E62-677, 100 Main Street, Cambridge, MA 02142, United States article info abstract Article history: I provide evidence that investors overweight analyst forecasts by demonstrating that Received 15 October 2011 prices do not fully reflect predictable components of analyst errors, which conflicts with Received in revised form conclusions in prior research. I highlight estimation bias in traditional approaches and 29 May 2012 develop a new approach that reduces this bias. I estimate characteristic forecasts that Accepted 12 June 2012 map current firm characteristics into forecasts of future earnings. Contrasting character- istic and analyst forecasts predicts analyst forecast errors and revisions. I find abnormal JEL classification: returns to strategies that sort firms by predicted forecast errors, consistent with investors G10 overweighting analyst forecasts and predictable biases in analyst forecasts influencing G11 the information content of prices. G14 & 2013 Elsevier B.V. All rights reserved. G17 G24 M41 Keywords: Analysts Forecast errors Earnings Market efficiency 1. Introduction Estimating a firm’s future profitability is an essential part of valuation analysis. Analysts can facilitate the $ I
    [Show full text]
  • Open Dau Nicholas Usingconsensusmodelingtoformulateamoreaccurateeconomicforecast.Pdf
    THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF ECONOMICS USING CONSENSUS MODELING TO FORMULATE A MORE ACCURATE ECONOMIC FORECAST NICHOLAS M. DAU SPRING 2015 A thesis submitted in partial fulfillment of the requirements for baccalaureate degrees in Economics and Political Science with honors in Economics Reviewed and approved* by the following: Andres Aradillas-Lopez Associate Professor of Economics Thesis Supervisor Russell Chuderewicz Senior Lecturer of Economics Honors Adviser * Signatures are on file in the Schreyer Honors College. i ABSTRACT This paper looks to forecast GDP growth, CPI change and 10-Year Treasury note interest rates through a consensus forecasting model. The consensus model takes into account 50-60 forecasts available from the Blue Chip Economic Indicators survey. The model differs from mainstream forecasts in that it assigns probabilities for each economic outcome, and supplements this with a broad 99% confidence interval. Ultimately the model performs best for current year CPI change and GDP growth to a lesser degree. The model struggles to provide precise and accurate forecasts for the long-term GDP, CPI and interest rate forecasts. ii TABLE OF CONTENTS LIST OF FIGURES ..................................................................................................... iii LIST OF TABLES ....................................................................................................... iv ACKNOWLEDGEMENTS ........................................................................................
    [Show full text]
  • A Re-Examination of Analysts' Superiority Over Time-Series Forecasts
    A re-examination of analysts’ superiority over time-series forecasts Mark T. Bradshaw Boston College Michael S. Drake The Ohio State University James N. Myers University of Arkansas Linda A. Myers University of Arkansas Draft: December 2009 Abstract: In this paper, we re-examine the widely-held belief that analysts’ earnings per share (EPS) forecasts are superior to forecasts from a time-series model. Using a naive random walk time-series model for annual earnings, we investigate whether and when analysts’ annual forecasts are superior. We also examine whether analysts’ forecasts approximate market expectations better than expectations from a simple random walk model. Our results indicate that simple random walk EPS forecasts are more accurate than analysts’ forecasts over longer forecast horizons and for firms that are smaller, younger, or have limited analyst following. Analysts’ superiority is less prevalent when analysts forecast large changes in EPS. These findings suggest an incomplete and misleading generalization about the superiority of analysts’ forecasts over even simple time-series-based earnings forecasts. Our findings imply that in certain settings, researchers can reliably use time-series-based forecasts in studies requiring earnings expectations. We thank workshop participants at the University of Akron, University of Arkansas, Florida State University, and Ohio State University for helpful suggestions and comments. James Myers gratefully acknowledges financial support from the Ralph L. McQueen Chair and Linda Myers gratefully acknowledges financial support from the Garrison/Wilson Chair, both at the University of Arkansas. 1. Introduction Research on analysts’ forecasts originated from a need within capital markets research to find a reliable proxy for investor expectations of earnings per share (EPS).
    [Show full text]
  • Partial Information Framework: Basic Theory and Applications
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2016 Partial Information Framework: Basic Theory and Applications Ville Antton Satopää University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Applied Mathematics Commons, Mathematics Commons, and the Statistics and Probability Commons Recommended Citation Satopää, Ville Antton, "Partial Information Framework: Basic Theory and Applications" (2016). Publicly Accessible Penn Dissertations. 1991. https://repository.upenn.edu/edissertations/1991 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1991 For more information, please contact [email protected]. Partial Information Framework: Basic Theory and Applications Abstract Many real-world decisions depend on accurate predictions of some future outcome. In such cases the decision-maker often seeks to consult multiple people or/and models for their forecasts. These forecasts are then aggregated into a consensus that is inputted in the final decision-making process. Principled aggregation requires an understanding of why the forecasts are different. Historically, such forecast heterogeneity has been explained by measurement error. This dissertation, however, first shows that measurement error is not appropriate for modeling forecast heterogeneity and then introduces information diversity as a more appropriate yet fundamentally different alternative. Under information diversity differences in the forecasts stem purely from differences in the information that is used in the forecasts. This is made mathematically precise in a new modeling framework called the partial information framework. At its most general level, the partial information framework is a very reasonable model of multiple forecasts and hence offers an ideal platform for theoretical analysis.
    [Show full text]
  • The IMF and OECD Versus Consensus Forecasts by Roy
    The IMF and OECD versus Consensus Forecasts by Roy Batchelor City University Business School, London August 2000 Abstract: This paper compares the accuracy and information content of macroeconomic economic forecasts for G7 countries made in the 1990s by the OECD and IMF. The benchmarks for comparison are the average forecasts of private sector economists published by Consensus Economics. With few exceptions, the private sector forecasts are less biased and more accurate in terms of mean absolute error and root mean square error. Formal tests show that there is little information in the OECD and IMF forecasts that could be used to reduce significantly the error in the private sector forecasts. Keywords: Forecasting, Combining Forecasts JEL Class No. : E27, E37 Acknowledgments: I am very grateful to Patrick R. Dennis for help in assembling the data used in this study. City University Business School Frobisher Crescent, Barbican, London EC2Y 8HB, UK. Phone:(-44-) 0-207 477 8733 Fax: (-44-) 0-207 477 8881 [email protected] 1 1. Introduction This study assesses the accuracy and value-added of the economic forecasts for G7 countries produced by the International Monetary Fund (IMF) and the Organization for Economic Cooperation and Development (OECD). The forecasts of the two agencies for the years 1990- 1999 are compared with a tough but relevant benchmark, the averages of private sector forecasts published by Consensus Economics. The study updates the comparisons made for an earlier period by Batchelor (2000). The quality of economic forecasts has been the subject of a broad debate in the press, the business community and government throughout the past decade.
    [Show full text]
  • Assessment of Consensus Forecasts Accuracy: the Czech National Bank Perspective
    JEL Classification: E37; E58 Keywords: forecasting accuracy; prediction process; survey forecast Assessment of Consensus Forecasts Accuracy: * The Czech National Bank Perspective Filip NOVOTNÝ ([email protected]) – corresponding author Marie RAKOVÁ both authors: Czech National Bank, Prague Abstract This paper compares the accuracy of the Consensus forecasts for euro-area GDP growth, consumer and producer price inflation, and the USD/EUR exchange rate to those of the European Commission, International Monetary Fund, and Organization for Eco- nomic Co-operation and Development, and also to the naive forecast and the forecast implied by the forward exchange rate. In the period from 1994 to 2009 the Consensus forecasts for effective euro-area consumer price inflation and GDP growth beat the alter- natives by a difference which is typically statistically significant. The results are more diverse for the pre-crisis sample (1994–2007). The Consensus forecast for euro-area producer price inflation significantly outperforms the naive forecast in the short term. Finally, the Consensus forecast for the USD/EUR exchange rate during the period from 2002 to 2009 is more precise than the naive forecast and the forecast implied by the for- ward rate. 1. Introduction The monetary policy regime of direct inflation targeting was adopted by the Czech National Bank (CNB) in 1998. Under inflation targeting, the forecast for consumer price inflation (CPI) at the monetary policy horizon is of great relevance to the decision-making on current interest rates. An important assumption of the CNB’s macroeconomic forecast is the external economic outlook, which is partly derived from the Consensus Forecasts (hereinafter “Consensus”).
    [Show full text]